Interwell Stratigraphic Correlation Detection based on knowledge-enhanced few-shot learning

Interwell Stratigraphic Correlations Detection (ISCD) guides reservoir modeling and oil development. Many existing AI (artificial intelligence) methods have been proposed for ISCD. However, it is difficult to generate labels for large-scale geological data, which leads to the problem of small samples. In this paper, we propose a few-shot learning-based approach to detect stratigraphic correlations for overcoming this challenge. Specifically, we design a Knowledge Enhanced Few-shot Transformer ISCD model (KEFT-ISCD) to enhance reservoir sample features. We design a dynamically balanced marginal softmax ($dbm\text{-}softmax$) to further optimize the model loss for identifying edge features, which improves the stratigraphic matching effects. In addition, we design a bi-window co-sliding approach to address the cross-matching problem in practical stratigraphic matching. To the best of our knowledge, this is the first work to use few-shot learning for the ISCD. We evaluate the proposed method with different well sections in a pair of adjacent wells from a real-world well logging dataset. Experimental results indicate that the proposed KEFT-ISCD performs well and achieves a detection accuracy of 91.12%. We also conduct experiments on different wells and blocks. The results further demonstrate the generalizability of the proposed approach.